How to Use the InfoVetted MCP in Google ADK

Q: Can google-adk trigger automated InfoVetted criminal record searches?

Yes, your agent can call createnewvettingcheck after retrieving candidate details. It can determine which search type is required by checking listsupportedchecktypes first.

Run candidate background checks directly from BigQuery using Google ADK and Gemini long-context reasoning.

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Connect InfoVetted MCP to Google ADK

Create your Vinkius account to connect InfoVetted to Google ADK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup InfoVetted with Google ADK

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Connect BigQuery candidate data to Google ADK

The `create_screening_contact` tool connects your BigQuery candidate data directly to your background check pipeline. With this MCP Server, your Google ADK agent can pull candidate profiles from your database and immediately register them. Gemini handles the transition from database record to active screening profile without manual data entry. Because Gemini supports massive context windows, your agent can analyze entire candidate histories alongside the background check results. It can call `get_contact_details` to feed detailed resumes and screening data into Vertex AI for deep compliance analysis.

Automate screening with this MCP Server

The `create_contact_group` tool lets your Gemini agent organize applicants into specific departments. Running high-volume hiring pipelines requires structured operations. Your agent can trigger the correct screening sequence by calling `create_new_vetting_check` for every new contact added to that group. You can control which tools are exposed using the toolset filters in the ADK. This MCP integration lets you restrict access to `list_screening_contacts` and `get_vetting_request_status`, keeping your write operations locked down.

Track and audit background check requests

The `list_vetting_requests` tool gives compliance officers a clear audit trail of all background checks. Your agent can query `list_vetting_requests` to compile a complete history of active and completed screenings. Cross-referencing this list with your GCP logs ensures every check matches an authorized hiring request. If a candidate disputes a check or withdraws their application, the agent can immediately invoke `cancel_active_vetting`. Reviewing `list_configured_webhooks` lets you verify that your Google Cloud Functions are correctly set up to receive real-time status updates.

Setup guide

Set up InfoVetted MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with InfoVetted tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="InfoVetted_agent",
    model="gemini-2.0-flash",
    instruction="You have access to InfoVetted tools via MCP.",
    tools=mcp_tools,
)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by InfoVetted. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about InfoVetted MCP in Google ADK

You initialize the toolset using the HTTP transport class with your Vinkius endpoint URL. Pass this MCP setup directly to your ADK agent, and Gemini will automatically gain the ability to call tools like `create_screening_contact`.

Yes, your agent can call `create_new_vetting_check` after retrieving candidate details. It can determine which search type is required by checking `list_supported_check_types` first.

The ADK processes tool calls asynchronously, allowing Gemini to manage multiple screenings at once. Your agent can list active checks with `list_vetting_requests` and process results in parallel using GCP infrastructure.

Yes, you can pass a list of allowed tool names to the ADK toolset parameter. This prevents the agent from calling tools like `cancel_active_vetting` while still allowing it to check statuses using `get_vetting_request_status`.

All sensitive personal information, including criminal record searches and education verification data, is processed inside ephemeral V8 isolates. The connection between Google ADK and Vinkius is secured via HTTPS, ensuring that candidate data is never cached or used to train the underlying Gemini models.

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